Data analysts and other people work with a wide variety of digital data, which is organized in various ways, and to various extents. Some data values are solitary, in the sense that they do not belong (or at least are not treated as belonging) to a set of related data values. But many data values are part of a collection of data values. Some collections have little or no internal structure, but other collections are organized to facilitate operations such as retrieval of particular values, comparison of values, and computational summaries based on multiple values of the collection.
An organized collection of data values is referred to herein as a “dataset” (a.k.a. “data set”, “structured data”, “structured dataset”). Because the data in a dataset is structured, one can say more about it than a mere recital of its value and its membership in the data set. In a spreadsheet dataset, for example, a given piece of data not only has a value and membership in the set of spreadsheet values, it also has an associated row and column, which may in turn have characteristics such as names and data types. For present purposes, some familiar examples of “structured data” include relational database records, spreadsheets, tables, and arrays. By contrast, the text in an email or a word processing document is generally unstructured data or lacks a standard internal structure. Structured data may be placed in a dataset manually by typing, but computational capability can provide a range of other possibilities for adding values to a data set, changing values in a data set, and otherwise managing datasets.